Page Rank: Theory and Convergence

Related lectures (32)

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Explores Markov chains, focusing on homogeneous processes and stationary distributions, with practical exercises.

Explores the lower bound on total variation distance in Markov chains and its implications on mixing time.

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.

Explains the proof of the ergodic theorem and the concept of positive-recurrence in Markov chains.

Covers the basics of Markov chains and their algorithmic applications.

Explores the spectral gap in Markov chains and its impact on convergence speed.

Explores limiting distribution in Markov chains and the implications of ergodicity and aperiodicity on stationary distributions.

Covers the concept of invariant measures in Markov chains and their role in analyzing irreducible recurrent processes.

Explores ergodic theory in Markov chains, discussing irreducibility and unique stationary distributions.

Explores the Bonus Malus system for insurance premiums and Markov chain transition probabilities.